Abstract: An unauthorized alteration in the viewpoint of a surveillance cameras is called tampering. This involves comparing images from the surveillance camera against a reference model. The reference model represents the features (e.g. background, edges, and interest points) of the image under normal operating conditions. The approach is to identify a tamper by analysing the distance between the features of the image from surveillance camera and from the reference model. If the distance is not within a certain threshold, the image is labeled as a tamper. Most methods have used images from the immediate past of the surveillance camera to construct the reference model. We propose to employ a generative model that learns the distribution of images from the surveillance camera under normal operating conditions, by training a generative adversarial network (GAN). The GAN is capable of sampling images from the probability density function, which are used as reference. We train a Siamese network that transforms the images into a feature space, so as to maximize the distance between the generated images and tampered images (while minimizing the distance between generated and normal images). The distance between the generated and the surveillance camera image is classified as either normal or tampered. The model is trained and tested over a synthetic dataset that is created by inducing artificial tampering (using image processing techniques). We compare the performance of the proposed model against two existing methods. Results show that the proposed model is highly capable of detecting and classifying tampering, and outperforms the existing methods with respect to accuracy and false positive rate.(More)

An unauthorized alteration in the viewpoint of a surveillance cameras is called tampering. This involves comparing images from the surveillance camera against a reference model. The reference model represents the features (e.g. background, edges, and interest points) of the image under normal operating conditions. The approach is to identify a tamper by analysing the distance between the features of the image from surveillance camera and from the reference model. If the distance is not within a certain threshold, the image is labeled as a tamper. Most methods have used images from the immediate past of the surveillance camera to construct the reference model. We propose to employ a generative model that learns the distribution of images from the surveillance camera under normal operating conditions, by training a generative adversarial network (GAN). The GAN is capable of sampling images from the probability density function, which are used as reference. We train a Siamese network that transforms the images into a feature space, so as to maximize the distance between the generated images and tampered images (while minimizing the distance between generated and normal images). The distance between the generated and the surveillance camera image is classified as either normal or tampered. The model is trained and tested over a synthetic dataset that is created by inducing artificial tampering (using image processing techniques). We compare the performance of the proposed model against two existing methods. Results show that the proposed model is highly capable of detecting and classifying tampering, and outperforms the existing methods with respect to accuracy and false positive rate.